Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
Readings in model-based diagnosis
Readings in model-based diagnosis
A machine program for theorem-proving
Communications of the ACM
Boosting complete techniques thanks to local search methods
Annals of Mathematics and Artificial Intelligence
Handling Inconsistency Efficiently in the Incremental Construction of Statified Belief Bases
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Redundancy in Random SAT Formulas
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
System Description: CRIL Platform for SAT
CADE-15 Proceedings of the 15th International Conference on Automated Deduction: Automated Deduction
Inconsistency management and prioritized syntax-based entailment
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Local-search Extraction of MUSes
Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
| Hi-index | 0.00 |
Local search is often a suitable paradigm for solving hard decision problems and approximating computationally difficult ones in the artificial intelligence domain. In this paper, it is shown that a smart use of the computation of a local search that failed to solve a NP-hard decision problem A can sometimes slash down the computing time for the resolution of computationally harder optimization problems containing A as a sub-problem. As a case study, we take A as SAT and consider some PNP[O(logm)] symbolic reasoning problems. Applying this technique, these latter problems can often be solved thanks to a small constant number of calls to a SAT-solver, only.